Mohit Aron

Ai : Hunanity's greatest creation for Good and Bad. To secure ourself #zerotrustai In the journey, MS brings up VASA-1 will AI model which essentially spins a selfie image and turn it into a talking clip of you. All you have to do is upload a photo along with a voice note and let the AI model do the talking for you.

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A Marriage Made in Heaven - LLM's and DADA-X by Decision-Zone The integration of task-oriented Large Language Models (LLMs) with the dynamic capabilities of DADA-X is indeed a transformative development in the realm of distributed systems management. These advanced LLMs, with their profound ability to simulate intricate Operational Technology (OT) systems, are revolutionizing the way mission-critical processes are conceived and executed. When these processes are fed into DADA-X, they are ingeniously converted into causal event patterns, which act as the backbone for autonomous applications operating in the real world. This convergence is unlocking unprecedented levels of real-time control and management. Organizations can deploy these sophisticated event patterns within DADA-X, enabling them to fully exploit the capabilities of their distributed systems. The result is a paradigm shift in operational efficiency, with DADA-X autonomously orchestrating complex missions, optimizing performance, and proactively mitigating risks. The emergence of the Artificial General Intelligence (AGI) market is a direct consequence of the synergy between LLMs and DADA-X. This market is poised to offer organizations the ability to construct intelligent systems that not only comprehend but also autonomously execute complex tasks with remarkable precision and efficiency. The potential here is immense, with implications across various sectors and industries. As we move forward, the traditional approaches of manual intervention and centralized control are becoming obsolete. The combination of LLMs and DADAX equips organizations to confidently step into the future of distributed systems management. This transition heralds the end of inefficiencies and the beginning of an era characterized by autonomy, efficiency, and reliability. The opportunity presented by this technological evolution is significant. Organizations that act swiftly can transform their distributed systems into agile, intelligent networks capable of managing the complexities of contemporary operations. The power of LLMs and DADA-X is not just theoretical but is a tangible force driving the AGI market forward. To be at the forefront of this revolution is to be a pioneer in the AGI market. It's an invitation for organizations to empower themselves with the necessary tools and technologies to succeed in an increasingly intricate and dynamic landscape. The future of distributed systems management has arrived, and it is powered by the innovative fusion of LLMs and DADA-X. The call to join this revolution is clear, and it promises a journey towards a smarter, more autonomous world.

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One of the key capabilities that sets Memgraph apart from the competition is our deep path traversal algorithms. Come read our latest blog explaining all about it: https://lnkd.in/gBtWPJuZ

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We sat down with the insightful Aman Garg to seek his informed perspectives on the role of #AI in #PreSales. He pointed out an important vector to consider while assessing AI's impact - customer segmentation. Consider customers on a spectrum: from large enterprises with seven-figure ARR commitments to high-volume, high-transaction customers who may just sign up for free trials and need deeper feature demos. AI's impact will be most significant in this high-volume mid-market segment. Here, the investment in AI tools offers the highest return due to the sheer volume of interactions. 💡 🚀

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The high-end graphics cards from Nvidia, such as the H100, have catalyzed the emergence of dedicated Rent-A-GPU farms, expanding beyond traditional hyperscale providers like AWS, GCP, and Azure. Initially, supply chain limitations coupled with high demand enabled these specialized farms to purchase these cards—each costing nearly $40,000—in bulk and offer them for rent. By late 2023, rates were around $8 per hour under commitments ranging from one to three years. Today, the landscape has shifted significantly. Some of the largest Rent-A-GPU providers have adapted to changing market dynamics by offering GPU spot markets with commitments as short as 48 hours, with rates as low as $1.8 per hour or even less. This suggests that the supply chain for high-end GPUs has finally caught up with demand, potentially heralding a substantial decrease in costs not only in the rental market but also in capital expenditures for owning these GPUs. For the first time, there is downward pressure on the prices of high-end GPUs. This development is particularly promising for real-world applications of Generation AI. The significant reduction in inference costs is crucial for the viability and value creation of GenAI within enterprises. It represents the first of many indicators that we are approaching the rapid realization of game-changing use cases, transforming how businesses leverage advanced AI capabilities. At KamiwazaAI we believe the start of an 5th Industrial Revolution Enterprise #5IR is around #1trillionInferences a day or in the range of ~$3b dollars a day utilizing chat GPT4 at .003 per Inference (100 tokens). A 1000x decrease in the cost of Inference is only 18 months away or so and its time NOW to begin implementing the technology to keep up!

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Here is my column on the #TiEcon2024 conference in #indicanews:

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*Mixture of Agents (MoA)* is an innovative way to boost Large Language Models’ performance by leveraging the strengths of multiple agents as a team. Layered Architecture: MoA arranges many LLMs in layered architecture, each layer composed of numerous individual agents(LLMs). Collaboration: These agents generate responses that are based on outputs from the previous layer’s agents. They repeatedly refine and enhance the last output. Diverse Insights: By integrating different capabilities and insights from various models, MoA achieves a more powerful combined model that is secure against diverse risks. Performance Boost: MoA outperforms standalone LLMs by a wide margin. For instance, it set a new benchmark with 65.1% on AlpacaEval 2.0 as against GPT-4 Omni at 57.5%, using only open-source models. In short, if we combine language modelers’ strengths then it results in increased efficiency and versatility. Here is a nice implementation of MOA by Together AI Github source: https://lnkd.in/dMCVRS3D #AI #LLM #LargeLanguageModels #MachineLearning

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At Corgnit Research Lab, India, we are excited to share our latest findings on the convergence of Reinforcement Learning and Federated Learning and their implications for advancing AGI. Key Insights: - 𝐒𝐜𝐚𝐥𝐚𝐛𝐢𝐥𝐢𝐭𝐲 𝐚𝐧𝐝 𝐄𝐟𝐟𝐢𝐜𝐢𝐞𝐧𝐜𝐲: The distributed nature of FL enables scalable and efficient training of RL models, significantly reducing computational overhead and accelerating convergence. - 𝐏𝐫𝐢𝐯𝐚𝐜𝐲-𝐏𝐫𝐞𝐬𝐞𝐫𝐯𝐢𝐧𝐠 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠: By ensuring that sensitive data remains local, FL facilitates the development of AGI in privacy-sensitve domains, such as healthcare, without compromising data security. - 𝐂𝐨𝐥𝐥𝐚𝐛𝐨𝐫𝐚𝐭𝐢𝐯𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠: Multiple RL agents can share knowledge through FL, enhancing overall learning efficiency and robustness without the need to share raw data. - 𝐏𝐞𝐫𝐬𝐨𝐧𝐚𝐥𝐢𝐳𝐞𝐝 𝐀𝐝𝐚𝐩𝐭𝐚𝐭𝐢𝐨𝐧: The integration of FL allows AGI models to be tailored to individual users or environments, combining global intelligence with local customization. - 𝐑𝐨𝐛𝐮𝐬𝐭𝐧𝐞𝐬𝐬 𝐚𝐧𝐝 𝐑𝐞𝐬𝐢𝐥𝐢𝐞𝐧𝐜𝐞: Aggregating diverse experiences through FL results in more generalized and resilient AGI models, mitigating the risk of overfitting. Benefits for Researchers: - 𝐑𝐞𝐬𝐨𝐮𝐫𝐜𝐞 𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐚𝐭𝐢𝐨𝐧: Leveraging distributed computational resources lowers costs and enhances the feasibility of large-scale AGI experiments. - 𝐄𝐧𝐡𝐚𝐧𝐜𝐞𝐝 𝐂𝐨𝐥𝐥𝐚𝐛𝐨𝐫𝐚𝐭𝐢𝐨𝐧: Model updates can be shared instead of raw data, promoting cross-institutional and interdisciplinary research. - 𝐀𝐜𝐜𝐞𝐥𝐞𝐫𝐚𝐭𝐞𝐝 𝐃𝐞𝐯𝐞𝐥𝐨𝐩𝐦𝐞𝐧𝐭: Faster training times and rapid iteration lead to quicker advancements in AGI. - 𝐄𝐭𝐡𝐢𝐜𝐚𝐥 𝐀𝐈 𝐃𝐞𝐯𝐞𝐥𝐨𝐩𝐦𝐞𝐧𝐭: Prioritizing user privacy and data security aligns with ethical standards and regulations, fostering responsible AI research. - 𝐑𝐞𝐚𝐥-𝐖𝐨𝐫𝐥𝐝 𝐀𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬: Immediate feedback and improvements from real-world data enable practical deployment in sectors like smart cities, healthcare, and IoT. Our research at Corgnit underscores the transformative potential of merging RL and FL in the pursuit of AGI. We believe these insights will catalyze further innovations and collaborations in the AI community. #ArtificialGeneralIntelligence #ReinforcementLearning #FederatedLearning #AIResearch #CorgnitResearchLab #PrivacyPreservingAI #ScalableAI #EthicalAI Corgnit, Corgnit Research India, Reinforcement Learning, Federated Learning Consortium

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What’s Next: RunWhen's Kyle Forster talks open source and microservices SDxCentral CEO Matt Palmer speaks with RunWhen's Kyle Forster about a better way for companies to support gigantic code bases.

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Let’s face it, LLMs aren’t without limitations. In this discussion, Varun Grover shares the benefits of a RAG framework and provides an overview of how it works as well as practical use cases. Watch now: https://vrt.as/43MNQQx #AI #GenAI #LLM #RAG

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How do you know the Last Mile of Your AI Delivery is Safe, Secure and Reliable. Come to Prashanth's talk and find out.

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Great insights in this piece on how AI Gateways can efficiently manage and secure all an organization's AI apps. #APIs #AppDelivery #Apps #containers #F5NGINX #kubernetes #NGINX #OpenSource #SaaS #ServiceMesh #WAF #ZeroTrust

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Great read on our journey to securing your AI investments from Steph Hay and Umesh Shankar #1 - AI opens up a world of possibilities. Be proactive in securing your AI investments - data, models, applications, and infrastructure. #2 - Make Google part of your security team, and Security Command Center - Enterprise as your default platform to help secure your AI investments. #3 - New capabilities in Notebook Scanner (private preview) and Model Armor (previewing soon) to secure your AI investments. Shout out to Amit Patil, Bharath Chandrasekhar, Matt Driscoll, Bhaskar Annadata, Cfir Cohen, Rahul Venkatraj and many more for these new capabilities.

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Be sure to check out Vamshidhar Kommineni and Vishnu Charan TJ as they demonstrate insane levels of throughput from Blob storage alongside our much improved Blobfuse client! (~8Tbps of ingress, ~14Tbps of egress) @ #MSBuild https://lnkd.in/g6S6TxPu #AzureStorage #AzureBlobStorage #BlobStorage

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Check out Amit Rathi on using Jupyter notebooks for DevOps. He does a great job communicating about the different dimensions that make notebooks and operations a perfect match. It's always so fun to find confirmation like this in the wild. https://lnkd.in/gjmGnfRb #runme #devops #jupyer

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Fascinating conversation.. The question is what percentage of future workloads will be running on HPC infrastructure vs customized CPU based server infrastructure.

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Lesson of the day at ODSC: yes, start with LLM but eventually you will need to train against the "monstrosity" of the great internet data with reinforcement learning from human feedback (RLHF). Great presentation Sanyam Bhutani in #ODSCEast #odsc!

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